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学者姓名：郭谋发
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Abstract ：
Identifying fault sections in single-phase ground (SPG) faults is essential for electric utilities to promptly isolate faults and restore service. A deep learning-based approach leveraging feature fusion has been proposed for SPG fault section location, utilizing transient zero-sequence currents (TZSCs) captured by feeder terminal units (FTUs). Initially, a convolutional neural network (ConvNet) is pre-trained on TZSC waveforms to distinguish data from the upstream and downstream of the fault point, acting as a feature extractor. This pre-training enables the model to capture distinct transient characteristics from both ends of the fault. The pre-trained ConvNet is then replicated to form a dual-branch architecture, where TZSC data from both ends of the feeder section are input into the respective branches. The features extracted from these branches are concatenated at a fusion layer, allowing the model to effectively integrate the transient information from upstream and downstream, leading to more precise fault section location. Compared with existing methods, our approach demonstrates robustness under various conditions, including simulation verification and field verification. Extensive testing shows that the model maintains high performance even with limited field data, and fine-tuning further enhances its practical applicability for engineering. Moreover, an industrial prototype utilizing Raspberry Pi 4B has been implemented in real-world distribution networks, where fault data are transmitted to the main station, further optimizing the fault section location process using our proposed approach.
Keyword ：
Fault section location Fault section location Feature fusion Feature fusion One-dimension convolutional neural network One-dimension convolutional neural network Resonant distribution networks Resonant distribution networks
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue et al. Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 268 . |
| MLA | Gao, Jian-Hong et al. "Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks" . | EXPERT SYSTEMS WITH APPLICATIONS 268 (2025) . |
| APA | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue , Chen, Duan-Yu , Bai, Hao . Deep learning approach for single-phase ground fault section location via feature fusion in resonant distribution networks . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 268 . |
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Complex single-phase ground fault (SPGF) is a challenging problem for early detection and type recognition in resonant distribution networks. This paper proposes a novel semantic-segmentation-based approach that leverages the morphological information of zero-sequence voltage signals to extract diverse semantic features representing fault inception (FI), fault disappearance (FD), and short-term transient fault (STF). A 1D-UNet model is employed to classify each sample point into one of these categories, which enables the determination of the moment and duration of SPGF. Based on these features, three types of SPGF are recognized: permanent fault (PF), long-term transient fault(LTF), and short-term transient fault (STF). Due to its low power consumption and costeffectiveness, an industrial prototype integrated with the proposed approach has been developed using a Raspberry Pi board. The proposed approach achieves an overall accuracy of over 94 % in classifying sample points across diverse categories. Specifically, the individual accuracies for detecting sample points belonging to FI, FD, and STF were 0.978, 0.968, and 0.971, respectively. From an engineering application perspective, the proposed approach effectively identifies the moment of fault occurrence, whether it is PF, LTF, or STF. The maximum, minimum, and median triggering deviations were 10.8 ms,-6.4 ms, and-0.4 ms, respectively, significantly outperforming existing methods in terms of fault moment triggering deviation. The experimental results demonstrate that the proposed approach works effectively for early detection and type recognition of SPGF, showcasing significant potential for further expansion and broader application.
Keyword ：
Early detection Early detection Resonant distribution networks Resonant distribution networks Semantic segmentation Semantic segmentation Single-phase ground fault Single-phase ground fault Type recognition Type recognition
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue et al. Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks [J]. | APPLIED SOFT COMPUTING , 2025 , 171 . |
| MLA | Gao, Jian-Hong et al. "Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks" . | APPLIED SOFT COMPUTING 171 (2025) . |
| APA | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue , Chen, Duan-Yu , Bai, Hao . Semantic-segmentation-based approach for early detection and type recognition of single-phase ground fault in resonant distribution networks . | APPLIED SOFT COMPUTING , 2025 , 171 . |
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In order to address the challenges posed by weak and variable high-impedance fault signals and limited data availability in practical distribution networks, a novel method for detecting high-impedance faults is proposed. Initially, a multi-head variational autoencoder model based on squeeze-excitation networks is employed to augment the small sample dataset. Subsequently, the data are filtered, and the temporal and frequency domain features are extracted, respectively. Considering the weak characteristics of high impedance fault features and the limitations of the proliferation model in generating comprehensive and effective fault features, a categorical boosting algorithm based on the gradient harmonized mechanism (GHM-CatBoost) is introduced. The GHM-CatBoost algorithm incorporates a gradient harmonized mechanism loss function to address the imbalance in attention between easily distinguishable and challenging samples, thereby mitigating the issue of overfitting. The research findings suggest that the data proliferation model can produce fault samples with a blend of simulation data diversity and measured data randomness, thereby enhancing the richness of the dataset. Furthermore, the fault recognition accuracy achieved by the proposed GHM-CatBoost model is notably high at 97.21%, outperforming its counterpart classifier model. Moreover, the efficacy of the proposed approach is validated through rigorous testing and comparative analysis. © 2025 Science Press. All rights reserved.
Keyword ：
Adaptive boosting Adaptive boosting Fault detection Fault detection Frequency domain analysis Frequency domain analysis Image segmentation Image segmentation Network coding Network coding Variational techniques Variational techniques
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| GB/T 7714 | Gao, Wei , He, Wenxiu , Guo, Moufa et al. Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements [J]. | High Voltage Engineering , 2025 , 51 (3) : 1135-1144 . |
| MLA | Gao, Wei et al. "Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements" . | High Voltage Engineering 51 . 3 (2025) : 1135-1144 . |
| APA | Gao, Wei , He, Wenxiu , Guo, Moufa , Bai, Hao . Detection Method of High-impedance Fault in Distribution Network Based on Uneven Small Samples from Actual Measurements . | High Voltage Engineering , 2025 , 51 (3) , 1135-1144 . |
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Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders.
Keyword ：
Active distribution networks Active distribution networks Adaptive transient process calibration Adaptive transient process calibration Fault localization Fault localization High impedance fault High impedance fault Multiscale correlation analysis Multiscale correlation analysis
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou -Fa , Lin, Shuyue et al. Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks [J]. | MEASUREMENT , 2024 , 229 . |
| MLA | Gao, Jian-Hong et al. "Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks" . | MEASUREMENT 229 (2024) . |
| APA | Gao, Jian-Hong , Guo, Mou -Fa , Lin, Shuyue , Chen, Duan -Yu . Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks . | MEASUREMENT , 2024 , 229 . |
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Detecting high-impedance faults (HIFs) in distribution networks poses a significant challenge for conventional relay devices due to low fault currents and various characteristics such as weaker fault features, distortion offset, and background noise interference. This article introduces a novel and streamlined method for HIF detection, which ingeniously integrates frequency-band energy curve (FBEC) analysis and Gaussian smoothing to extract trend changes, thus enhancing the precision and effectiveness of HIF detection. The proposed method utilizes continuous wavelet transform (CWT) to extract the time-frequency spectrum from the zero-sequence current. By analyzing the feature-band energy, the FBEC is computed. To mitigate noise interference and enhance the periodic change pattern, a Gaussian filter is applied for smoothing. Distinguishing between HIF and normal operations, including low impedance fault (LIF), capacitor switching (CS), inrush current (IC), and ferromagnetic resonance (FR), is achieved by analyzing the peak points of FBEC. The proposed method's performance was extensively validated through simulations and field data. The performance of the proposed method was extensively validated through a series of simulations and detailed analysis of real-world field data. The results demonstrated an excellent detection performance on field data, with an impressive accuracy rate of 86.5% and an F-1 -score of 0.87. Moreover, we examined the method's resilience against noise using data with a signal-to-noise ratio (SNR) of 20 dB, resulting in a detection accuracy of 77.3% and an F-1 -score of 0.79. These findings underscore the method's clear physical meaning, strong interpretability, and versatility, establishing its effectiveness and practicality for real-world applications.
Keyword ：
Continuous wavelet transform (CWT) Continuous wavelet transform (CWT) distribution network distribution network Gaussian smoothing Gaussian smoothing high-impedance fault (HIF) high-impedance fault (HIF)
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| GB/T 7714 | Bai, Hao , Gao, Jian-Hong , Li, Wei et al. Detection of High-Impedance Fault in Distribution Networks Using Frequency-Band Energy Curve [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (1) : 427-436 . |
| MLA | Bai, Hao et al. "Detection of High-Impedance Fault in Distribution Networks Using Frequency-Band Energy Curve" . | IEEE SENSORS JOURNAL 24 . 1 (2024) : 427-436 . |
| APA | Bai, Hao , Gao, Jian-Hong , Li, Wei , Wang, Kang , Guo, Mou-Fa . Detection of High-Impedance Fault in Distribution Networks Using Frequency-Band Energy Curve . | IEEE SENSORS JOURNAL , 2024 , 24 (1) , 427-436 . |
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Accurate modeling of high-impedance arc fault (HIAF) is of great significance for studying the characteristics of arc fault and suppressing its harm. In traditional arc models, the arc column is usually considered a cylindrical channel with constant length and diameter. However, the arc-burning process is susceptible to the environment. The changes in arc length and diameter present complex characteristics. Therefore, this study proposes a new HIAF model for distribution networks based on the arc's dynamic geometry dimension (DGD). First, the arc length, diameter, and field strength expressions are improved based on the classical cybernetic model. Next, an automatic parameter optimization method for the DGD model is proposed, and then this model is compared with existing advanced models. After that, the effects of the variable parameters of this model on arc characteristics are analyzed. Finally, the practical application effect of this model is tested. The experimental results show that the DGD model can accurately describe the dynamic arc development process, approximate the given arc waveform closely, and generate high-quality samples, which has certain advantages.
Keyword ：
Arc model Arc model Distribution network Distribution network Geometry size Geometry size High-impedance arc fault High-impedance arc fault Model parameter determination Model parameter determination
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| GB/T 7714 | Gao, Wei , He, Wen-Xiu , Wai, Rong-Jong et al. High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension [J]. | ELECTRIC POWER SYSTEMS RESEARCH , 2024 , 229 . |
| MLA | Gao, Wei et al. "High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension" . | ELECTRIC POWER SYSTEMS RESEARCH 229 (2024) . |
| APA | Gao, Wei , He, Wen-Xiu , Wai, Rong-Jong , Zeng, Xiao-Feng , Guo, Mou-Fa . High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension . | ELECTRIC POWER SYSTEMS RESEARCH , 2024 , 229 . |
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The existing single-phase grounding (SPG) fault section location methods typically suffer from difficulty in feature selection, limited feeder terminal units (FTUs) configuration, and excessive dependence on communication, which weaken their generalization and robustness. To overcome these challenges, an SPG fault section location approach based on feature subset optimization is proposed. First, the relation between the position of FTU and its three-phase current variation is analyzed, and its fault features are extracted to construct the candidate feature sets as feature subset optimization objects. Then, genetic algorithm and support vector machine (SVM) are combined to select the optimal feature subset with small dimensions and recognition error, which avoids the empirical errors of artificial feature selection. To reduce the cumulative errors, the SVM hyperparameters are simultaneously optimized. Finally, the SVM model is trained based on the optimal feature subset and hyperparameters. In the absence of zero-sequence current measurement, three-phase currents measured by FTU are locally processed to locate the fault section by the trained SVM. The experimental results verified the effectiveness and feasibility of the proposed method. In this paper, a single-phase grounding fault section location method is proposed by using genetic algorithm and support vector machine (SVM) to achieve feature subset optimization. In the process of section identification, the empirical error caused by artificially selecting features is avoided, and cumulative errors of multiple links such as feature selection, parameter optimization, and model training are reduced. Additionally, the communication and measurement requirements are reduced since feeder terminal unit only needs to measure local three-phase current information. image
Keyword ：
distribution networks distribution networks fault section location fault section location feature subset optimization feature subset optimization genetic algorithm (GA) genetic algorithm (GA) single-phase grounding fault single-phase grounding fault support vector machine (SVM) support vector machine (SVM)
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| GB/T 7714 | Bai, Hao , Chen, Mu-Yan , Guo, Mou-Fa et al. Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation [J]. | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 , 52 (9) : 4582-4599 . |
| MLA | Bai, Hao et al. "Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation" . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 52 . 9 (2024) : 4582-4599 . |
| APA | Bai, Hao , Chen, Mu-Yan , Guo, Mou-Fa , Liu, Yi-Peng , Gao, Jian-Hong . Fault section location in resonant grounding distribution systems based on feature subset optimization of phase current variation . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 , 52 (9) , 4582-4599 . |
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Aiming at the problem that the existing single-phase-to-ground fault flexible compensation device has low compensation accuracy, long response time, and needs DC side power supply, a flexible compensation device based on double-loop passivity-based control is proposed. Firstly, the topology and working principle of the device are analyzed. And a compensation current distribution method of the bridge arm is proposed. Secondly, a double-loop passivity-based controller is designed. Finally, the proposed method is verified by MATLAB/Simulink software and 10 kV physical simulation system of distribution network. The comparisons between doubleloop passivity-based control, passivity-based control, and proportional integration control is discussed. The result shows that the proposed method can accurately and rapidly compensate the fault current.
Keyword ：
Distribution networks Distribution networks Double -loop passivity -based control Double -loop passivity -based control Flexible compensation Flexible compensation Single -phase -to -ground fault Single -phase -to -ground fault T -type topology T -type topology
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| GB/T 7714 | Guo, Mou-fa , Liu, Xin-bin , You, Jian-zhang et al. A flexible compensation device for single-phase-to-ground fault in distribution networks based on double-loop passivity-based control [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| MLA | Guo, Mou-fa et al. "A flexible compensation device for single-phase-to-ground fault in distribution networks based on double-loop passivity-based control" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 157 (2024) . |
| APA | Guo, Mou-fa , Liu, Xin-bin , You, Jian-zhang , Zheng, Ze-yin , Wang, Zhi-ying . A flexible compensation device for single-phase-to-ground fault in distribution networks based on double-loop passivity-based control . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
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In addressing the quantization noise challenge in high impedance fault (HIF) localization within resonant distribution networks, we propose a cutting-edge, explainable deep learning approach that significantly advances existing methods. This approach utilizes differential zero-sequence voltage (DZSV) and zero-sequence current (ZSC) and introduces a novel "Vague" classification to improve localization accuracy by effectively managing quantization noise-distorted signals. This approach extends beyond the conventional binary classification of "Fault" and "Sound," incorporating a multi-scale feature attention (MFA) mechanism for enriched internal explainability and applying gradient-weighted class activation mapping (Grad-CAM) to visualize critical input areas precisely. Our model, validated in an industrial prototype, exhibits unparalleled adaptability across various environmental conditions, including environmental noise, variable sampling rates, and triggering deviations. Comparative analysis reveals that our approach outperforms existing methods in managing diverse fault scenarios. This paper presents an explainable deep learning model for localizing high impedance faults (HIF) in resonant distribution networks, addressing challenges posed by quantization noise. By incorporating a novel 'Vague' classification and a multi-scale feature attention mechanism, the model improves fault localization accuracy. Validated in industrial applications, the model demonstrates robustness to environmental noise, sampling rate variations, and triggering deviations.image
Keyword ：
explainable deep learning explainable deep learning fault localization fault localization high impedance fault high impedance fault quantization noise quantization noise resonant distribution networks resonant distribution networks
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| GB/T 7714 | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue et al. Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise [J]. | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 . |
| MLA | Gao, Jian-Hong et al. "Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise" . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS (2024) . |
| APA | Gao, Jian-Hong , Guo, Mou-Fa , Lin, Shuyue , Hong, Qiteng . Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise . | INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS , 2024 . |
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大型发电机发生定子单相接地故障可能会产生电弧,导致铁心烧损,甚至引发火灾等严重事故,提出一种柔性融合消弧方法,通过并行控制中性点电压和变流器注入电流,实现接地故障电流抑制和可靠消弧.现有发电机定子单相接地柔性消弧方法在低阻或高阻的情况下消弧效果不佳,所提方法考虑了发电机的定子阻抗和定子并联分支数,适用于不同的发电机,在不同接地故障过渡电阻、不同故障位置等情况下,均能够将接地故障电流快速抑制到安全范围内.Matlab/Simulink仿真验证了所提方法的有效性.
Keyword ：
大型发电机 大型发电机 定子接地故障 定子接地故障 柔性消弧 柔性消弧 电压消弧 电压消弧 电流消弧 电流消弧 融合消弧 融合消弧
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| GB/T 7714 | 黄俊杰 , 郭谋发 , 张彬隆 . 大型发电机定子接地故障柔性融合消弧方法 [J]. | 电网技术 , 2024 , 48 (11) : 4748-4757 . |
| MLA | 黄俊杰 et al. "大型发电机定子接地故障柔性融合消弧方法" . | 电网技术 48 . 11 (2024) : 4748-4757 . |
| APA | 黄俊杰 , 郭谋发 , 张彬隆 . 大型发电机定子接地故障柔性融合消弧方法 . | 电网技术 , 2024 , 48 (11) , 4748-4757 . |
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